To select rows whose column value equals a scalar, some_value, use ==:
df.loc[df['column_name'] == some_value]
To select rows whose column value is in an iterable, some_values, use isin:
df.loc[df['column_name'].isin(some_values)]how-do-i-sort-a-dictionary-by-value
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0} dict(sorted(x.items(), key=lambda item: item[1]))how-can-i-count-the-occurrences-of-a-list-item
from collections import Counter l = ["a","b","b"] Counter(l)pandas.DataFrame.drop_duplicates
df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) df.drop_duplicates(subset=['brand'])tf.data.Dataset-----as_numpy_iterator()
Returns an iterator which converts all elements of the dataset to numpy.
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) for element in dataset.as_numpy_iterator(): print(element)tf.data.Dataset
The tf.data.Dataset API supports writing descriptive and efficient input pipelines. Dataset usage follows a common pattern:
Create a source dataset from your input data.Apply dataset transformations to preprocess the data.Iterate over the dataset and process the elements.Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.
The simplest way to create a dataset is to create it from a python list:
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) for element in dataset: print(element)
Once you have a dataset, you can apply transformations to prepare the data for your model:
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) dataset = dataset.map(lambda x: x*2) list(dataset.as_numpy_iterator())
本文转载自网络,原文链接:https://mp.weixin.qq.com/s/vlOUg46B5bcmToX-fjavJQ...
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